Chromagrams capture the harmonic content by showing how energy is distributed across the 12 pitch classes (C, C#, D, etc.) over time.
This took quite a lot of time to display this graphic, you may set 'fastdisp=TRUE' for a faster, but less accurate, display
pdf
2
This took quite a lot of time to display this graphic, you may set 'fastdisp=TRUE' for a faster, but less accurate, display
pdf
2
These tracks were created using Stableaudio AI (Stableaudio). I took inspiration from genre tags on RateYourMusic and carefully crafted prompts using detailed descriptors to shape the sound. After generating the tracks, I simply downloaded the MP3 files.
Track 1: Meditative Ambient Soundscape
Style: Ambient, Post-Rock, Cinematic
Length: 2 minutes
Goal: A calm, meditative ambient with minimal
instrumentation.
Tags Used:
Ambient, Post-Rock, Cinematic, Ethereal, Soothing, Meditative,
Minimalist, Warm Subtle Bass, Deep Drones, Airy Pads, Textures, Analog
Synths, Field Recordings, Wind Sounds, Reverb, 60
BPM
Track 2: Energetic Breakbeat Rave
Style: Breakbeat, Acid Breaks, 90s Rave
Length: 2 minutes
Goal: A high-energy, chaotic breakbeat track.
Tags Used:
Breakbeat, Acid Breaks, 90s Rave, Energetic, Raw, Funky, Chaotic,
Breakbeats, Deep Bass, Distorted 808, Acid Bass, Filtered Chords,
Reversed Pads, Vocal Chops, 135 BPM
Here’s a scatterplot of the Danceability compared to the Tempo of the tracks. My track 1 (ambient) is marked red, track 2 (breakbeat) is blue.
Findings and Final Thoughts
There appears to be no set correlation between the danceability and tempo of the tracks. However, an interesting pattern emerges: there are two clusters—one with low danceability, and another with high danceability, while the tempo does not differ much.
Regarding my own tracks:
One particularly surprising observation is how the AI interpreted the second song’s tempo. While I set it to 135 BPM, it was classified as 93 BPM. This suggests that the AI might have emphasized a different rhythmic structure or half-time feel in its classification.